Another important component of contrastive self-supervised leaning is the use of augmentations. One of the novelties in our proposed method is the use of frame sampling -based temporal augmentations along with popular spatial augmentations. Table REF  shows the ablation study on spatial and temporal augmentations used in our method, where the impact of each augmentation is investigated by systematic exclusion. We see that the temporal augmentation clearly has the highest impact on the final performance as removing this augmentation module reduces the accuracy of the model by about 4%. Among the spatial augmentations, `random color distortion' shows the highest impact, which is in conformity with other works on contrastive learning [1]}.
